Data set accompanying "Full-Field Digital Image Correlation (DIC) and Infrared Thermography (IR) Data for Seven Unique Geometries of 304L Stainless Steel Sheet Metal"

Material Testing 2.0 (MT2.0) is a paradigm that advocates for the use of rich, full-field data, such as from digital image correlation (DIC) and infrared (IR) thermography, for material characterization and material model calibration. By employing heterogeneous, multi-axial data in conjunction with sophisticated inverse calibration techniques such as finite element model updating (FEMU) and the virtual fields method (VFM), MT2.0 aims to reduce the number of specimens needed for material identification and to increase confidence in the calibration results. To support continued development, improvement, and validation of such inverse methods---specifically for rate-dependent, temperature-dependent, and anisotropic metal plasticity models---we provide here a thorough experimental data set for 304L stainless steel sheet metal. The data set includes full-field displacement, strain, and temperature data for seven unique specimen geometries tested at different strain rates and in different material orientations, with repeats of each test condition. Moreover, data is provided in both the raw format, as well as a post-processed format where everything has been synchronized temporally and registered spatially. The raw data allows consumers the opportunity to tailor the processing workflow for their specific applications using software of their choice, while the post-processed data allows consumers to directly employ the data without the need to reprocess it. Additionally, extensometer strain data is provided for tensile dog bones tested at three strain rates and in three material orientations, facilitating comparisons between so-called traditional calibration approaches and emerging novel calibration methods. A complete description of the experimental and post-process analysis methods, as well as a description of the data contained in this data set and the folder organization structure of the data set, is found in the accompanying journal article published in Scientific Data (https://doi.org/10.1038/s41597-024-03949-y). We believe this complete data set will be a valuable contribution to the experimental and computational mechanics communities, supporting continued advances in material identification methods.

材料测试2.0（MT2.0）倡导采用丰富的全场数据，如数字图像相关（DIC）和红外（IR）热成像技术，以实现材料表征和材料模型校准。通过结合异构、多轴数据与复杂的逆校准技术，如有限元模型更新（FEMU）和虚拟场方法（VFM），MT2.0旨在减少材料识别所需的样本数量，并提高校准结果的可信度。为了支持此类逆方法的持续发展、改进和验证——特别是对于速率依赖性、温度依赖性和各向异性金属塑性模型——我们在此提供了一份详尽的实验数据集，用于304L不锈钢板材。该数据集包括七个独特样本几何形状在不同应变率和不同材料取向下的全场位移、应力和温度数据，每个测试条件均有重复。此外，数据以原始格式和经过后处理的格式提供，后者实现了时间同步和空间配准。原始数据使消费者有机会使用他们选择的软件定制处理工作流程，而经过后处理的数据则允许消费者直接使用数据，无需重新处理。此外，还提供了在三种应变率和三种材料取向下测试的拉伸狗骨的引伸计应变数据，便于比较所谓的传统校准方法和新兴的校准方法。实验和后处理分析方法的完整描述，以及数据集内数据的内容描述和数据集文件夹的组织结构，可在随附于《科学数据》杂志的文章中找到（https://doi.org/10.1038/s41597-024-03949-y）。我们相信，这一完整的数据集将对实验和计算力学界做出宝贵的贡献，支持材料识别方法的持续进步。